code: def forward(self, data):
      x = data.node_feature
      edge_index = data.edge_index
      x = self.convs1(x, edge_index)
      x = forward_op(x, self.bns1)
      x = forward_op(x, self.relus1)
      x = self.convs2(x, edge_index)
      x = forward_op(x, self.bns2)
  
      pred = {}
      for message_type in data.edge_label_index:
          nodes_first = torch.index_select(
              x["n1"],
              0,
              data.edge_label_index[message_type][0, :].long(),
          )
          nodes_second = torch.index_select(
              x["n1"],
              0,
              data.edge_label_index[message_type][1, :].long(),
          )
          pred[message_type] = torch.sum(
              nodes_first * nodes_second, dim=-1
          )
      return pred
  
imports: import argparse
  import copy
  import time
  import numpy
  import networkx
  import sklearn.metrics
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch_geometric.datasets import TUDataset
  import torch_geometric.transforms
  import torch_geometric.nn
  from deepsnap.hetero_graph import HeteroGraph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  from deepsnap.hetero_gnn import HeteroSAGEConv, HeteroConv, forward_op
label_desc: Use image node_id 2 for calling the HeteroGNN obj's underlying member method code with example usage: obj.forward(data) and returns: pred